From 8f99f104ec47273121e40dbcfc54955a741e0254 Mon Sep 17 00:00:00 2001 From: LeiWang Date: Fri, 19 Apr 2024 02:54:25 -0400 Subject: [PATCH] Update README.md with corrected links and paths --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 2d094b501..122e99961 100644 --- a/README.md +++ b/README.md @@ -68,16 +68,16 @@ We are continuously expanding the support matrix. If you have any specific requi ## Getting Started -- [Installation](./docs/Installation.md): - To install BitBLAS, please checkout the document [installation](./docs/Installation.md). Also Make sure you already have the cuda toolkit (version >= 11) installed in the system. Or you can easily install from `pip install bitblas` in the root directory. +- [Installation](https://github.com/microsoft/BitBLAS/blob/main/docs/Installation.md): + To install BitBLAS, please checkout the document [installation](https://github.com/microsoft/BitBLAS/blob/main/docs/Installation.md). Also Make sure you already have the cuda toolkit (version >= 11) installed in the system. Or you can easily install from `pip install bitblas` in the root directory. -- [QuickStart](./docs/QuickStart.md): BitBLAS provides two Python APIs to perform mixed-precision matrix multiplication: +- [QuickStart](https://github.com/microsoft/BitBLAS/blob/main/docs/QuickStart.md): BitBLAS provides two Python APIs to perform mixed-precision matrix multiplication: - ```bitblas.Matmul``` implements the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication of $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$. - ```bitblas.Linear``` is a PyTorch ```nn.Linear```-like module to support a Linear of mixed-precision. -- [Integration](./integration/): Explore how BitBLAS seamlessly integrates with LLM deployment frameworks through our examples. Discover the ease of integrating BitBLAS with PyTorch, AutoGPTQ, and vLLM in the 3rd-party integration examples. +- [Integration](https://github.com/microsoft/BitBLAS/tree/main/integration): Explore how BitBLAS seamlessly integrates with LLM deployment frameworks through our examples. Discover the ease of integrating BitBLAS with PyTorch, AutoGPTQ, and vLLM in the 3rd-party integration examples. -- [Customization](./docs/ExtendOperatorsWithDSL.md): BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script). +- [Customization](https://github.com/microsoft/BitBLAS/blob/main/docs/ExtendOperatorsWithDSL.md): BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script). ## Contributing